Development of Cloud and Precipitation Property Retrieval Algorithms and Measurement Simulators from ASR Data
- Univ. of Utah, Salt Lake City, UT (United States). Dept. of Atmospheric Sciences
What has made the ASR program unique is the amount of information that is available. The suite of recently deployed instruments significantly expands the scope of the program (Mather and Voyles, 2013). The breadth of this information allows us to pose sophisticated process-level questions. Our ASR project, now entering its third year, has been about developing algorithms that use this information in ways that fully exploit the new capacity of the ARM data streams. Using optimal estimation (OE) and Markov Chain Monte Carlo (MCMC) inversion techniques, we have developed methodologies that allow us to use multiple radar frequency Doppler spectra along with lidar and passive constraints where data streams can be added or subtracted efficiently and algorithms can be reformulated for various combinations of hydrometeors by exchanging sets of empirical coefficients. These methodologies have been applied to boundary layer clouds, mixed phase snow cloud systems, and cirrus.
- Research Organization:
- Univ. of Utah, Salt Lake City, UT (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- DOE Contract Number:
- SC0007059
- OSTI ID:
- 1237457
- Report Number(s):
- DOE-UTAH-7059
- Country of Publication:
- United States
- Language:
- English
Similar Records
Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements
Use of DOE SGP Radars in Support of ASR Modeling Activities